Performance Analysis of Machine Learning Models for Object Recognition in Underwater Video Images

In this study, our primary aim is to detect different formations, objects on the images taken from various underwater videos. For this purpose, machine learning models such as SVM, multi-layer perceptron, logistic regression that use attributes, image histogram obtained from images were chosen. In addition, Autoencoder and CNN based deep learning models were used directly over images and their performances were compared. According to the results, it was observed that all models were satisfactory and achieved good classification performances. The highest performance was observed in the Autoencoder based deep learning model, which achieved an accuracy level of %95. In the future, we are planning to continue studies to focus on underwater cable tracking and detecting errors and anomalies in underwater cables.